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Propensity score methods are popular to control for confounding in observational biomedical studies of risk factors or medical treatments. This paper focused on aspects of propensity score methods that often remain undiscussed, including unmeasured confounding, missing data, variable selection, statistical efficiency, estimands, the positivity assumption, and predictive performance of the propensity score model., Competing Interests: Conflict of interest: R.H.H.G. and S.l.C. report no conflicts of interest. O.M.D. is a deputy editor for European Journal of Endocrinology. He was not involved in the review or editorial process for this paper, on which he is listed as an author., (© The Author(s) 2024. Published by Oxford University Press on behalf of European Society of Endocrinology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Objectives: It is unknown which confounding adjustment methods are currently used in the field of cardiothoracic surgery and whether these are appropriately applied. The aim of this study was to systematically evaluate the quality of conduct and reporting of confounding adjustment methods in observational studies on cardiothoracic interventions., Methods: A systematic review was performed, which included all observational studies that compared different interventions and were published between 1 January and 1 July 2022, in 3 European and American cardiothoracic surgery journals. Detailed information on confounding adjustment methods was extracted and subsequently described., Results: Ninety-two articles were included in the analysis. Outcome regression (n = 49, 53%) and propensity score (PS) matching (n = 44, 48%) were most popular (sometimes used in combination), whereas 11 (12%) studies applied no method at all. The way of selecting confounders was not reported in 42 (46%) of the studies, solely based on previous literature or clinical knowledge in 14 (16%), and (partly) data-driven in 25 (27%). For the studies that applied PS matching, the matched cohorts comprised on average 46% of the entire study population (range 9-82%)., Conclusions: Current reporting of confounding adjustment methods is insufficient in a large part of observational studies on cardiothoracic interventions, which makes quality judgement difficult. Appropriate application of confounding adjustment methods is crucial for causal inference on optimal treatment strategies for clinical practice. Reporting on these methods is an important aspect of this, which can be improved., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery.)

Proper adjustment for confounding is essential when estimating the effects of treatments or risk factors on health outcomes in observational data. To this end, various statistical methods have been developed. In the past couple of years, the use of propensity scores (PSs) to control for confounding has increased. Proper understanding of this method is necessary to critically appraise research in which it is applied. In this article, we provide an overview of PS methods, explaining their concept, advantages and possible disadvantages. Furthermore, the use of PS matching, PS adjustment and PS weighting is illustrated using data from the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD) cohort of dialysis patients., (© The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

This article gives a review of the limitations of propensity score matching as a tool for confounding control in the presence of censoring. Using an illustrative simulation study, we emphasize the importance of explicit adjustment for selective loss to follow-up and explain how this may be achieved.

Objectives: To assess the current practice of propensity score (PS) analysis in the medical literature, particularly the assessment and reporting of balance on confounders., Study Design and Setting: A PubMed search identified studies using PS methods from December 2011 through May 2012. For each article included in the review, information was extracted on important aspects of the PS such as the type of PS method used, variable selection for PS model, and assessment of balance., Results: Among 296 articles that were included in the review, variable selection for PS model was explicitly reported in 102 studies (34.4%). Covariate balance was checked and reported in 177 studies (59.8%). P-values were the most commonly used statistical tools to report balance (125 of 177, 70.6%). The standardized difference and graphical displays were reported in 45 (25.4%) and 11 (6.2%) articles, respectively. Matching on the PS was the most commonly used approach to control for confounding (68.9%), followed by PS adjustment (20.9%), PS stratification (13.9%), and inverse probability of treatment weighting (IPTW, 7.1%). Balance was more often checked in articles using PS matching and IPTW, 70.6% and 71.4%, respectively., Conclusion: The execution and reporting of covariate selection and assessment of balance is far from optimal. Recommendations on reporting of PS analysis are provided to allow better appraisal of the validity of PS-based studies., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background: Conditional on the propensity score (PS), treated and untreated subjects have similar distribution of observed baseline characteristics when the PS model is appropriately specified. The performance of several PS balance measures in assessing the balance of covariates achieved by a specific PS model and selecting the optimal PS model was evaluated in simulation studies. However, these studies involved only normally distributed covariates. Comparisons in binary or mixed covariate distributions with rare outcomes, typical of pharmacoepidemiologic settings, are scarce., Methods: Monte Carlo simulations were performed to examine the performance of different balance measures in terms of selecting an optimal PS model, thus reduction in bias. The balance of covariates between treatment groups was assessed using the absolute standardized difference, the Kolmogorov-Smirnov distance, the Lévy distance, and the overlapping coefficient. Spearman's correlation coefficient (r) between each of these balance measures and bias were calculated., Results: In large sample sizes (n ≥ 1000), all balance measures were similarly correlated with bias (r ranging between 0.50 and 0.68) irrespective of the treatment effect's strength and frequency of the outcome. In smaller sample sizes with mixed binary and continuous covariate distributions, these correlations were low for all balance measures (r ranging between 0.11 and 0.43), except for the absolute standardized difference (r = 0.51)., Conclusions: The absolute standardized difference, which is an easy-to-calculate balance measure, displayed consistently better performance across different simulation scenarios. Therefore, it should be the balance measure of choice for measuring and reporting the amount of balance reached, and for selecting the final PS model., (Copyright © 2014 John Wiley & Sons, Ltd.)

In their recent Health Services Research article titled "Squeezing the Balloon: Propensity Scores and Unmeasured Covariate Balance," Brooks and Ohsfeldt (2013) addressed an important topic on the balancing property of the propensity score (PS) with respect to unmeasured covariates. They concluded that PS methods that balance measured covariates between treated and untreated subjects exacerbate imbalance in unmeasured covariates that are unrelated to measured covariates. Furthermore, they emphasized that for PS algorithms, an imbalance on unmeasured covariates between treatment and untreated subjects is a necessary condition to achieve balance on measured covariates between the groups. We argue that these conclusions are the results of their assumptions on the mechanism of treatment allocation. In addition, we discuss the underlying assumptions of PS methods, their advantages compared with multivariate regression methods, as well as the interpretation of the effect estimates from PS methods., (© Health Research and Educational Trust.)

A propensity score is the probability of being treated or exposed, given measured confounders such as age. Propensity score methods can control for measured confounders in observational research, but not for unmeasured confounders. Study participants with the same propensity score will, on average, have a similar distribution of measured confounders. The propensity score can be used to control for confounding by means of stratification, matching, including the propensity score as a covariate in a multivariable regression model, or by weighting the study population using the propensity score. Propensity score methods can often control for more confounders than other methods, particularly in the case of a rare outcome. Reports on propensity score methods should mention which confounders were included in the propensity score and to what extent confounders were balanced across study groups, after stratification based on the propensity score.

Purpose: Propensity score (PS) methods aim to control for confounding by balancing confounders between exposed and unexposed subjects with the same PS. PS balance measures have been compared in simulated data but limited in empirical data. Our objective was to compare balance measures in clinical data and assessed the association between long-acting inhalation beta-agonist (LABA) use and myocardial infarction., Methods: We estimated the relationship between LABA use and myocardial infarction in a cohort of adults with a diagnosis of asthma or chronic obstructive pulmonary disorder from the Utrecht General Practitioner Research Network database. More than two thousand PS models, including information on the observed confounders age, sex, diabetes, cardiovascular disease and chronic obstructive pulmonary disorder status, were applied. The balance of these confounders was assessed using the standardised difference (SD), Kolmogorov-Smirnov (KS) distance and overlapping coefficient. Correlations between these balance measures were calculated. In addition, simulation studies were performed to assess the correlation between balance measures and bias., Results: LABA use was not related to myocardial infarction after conditioning on the PS (median heart rate = 1.14, 95%CI = 0.47-2.75). When using the different balance measures for selecting a PS model, similar associations were obtained. In our empirical data, SD and KS distance were highly correlated balance measures (r = 0.92). In simulations, SD, KS distance and overlapping coefficient were similarly correlated to bias (e.g. r = 0.55, r = 0.52 and r = -0.57, respectively, when conditioning on the PS)., Conclusions: We recommend using the SD or the KS distance to quantify the balance of confounder distributions when applying PS methods., (Copyright © 2011 John Wiley & Sons, Ltd.)

Purpose: Propensity score (PS) methods focus on balancing confounders between groups to estimate an unbiased treatment or exposure effect. However, there is lack of attention in actually measuring, reporting and using the information on balance, for instance for model selection. We propose to use a measure for balance in PS methods and describe several of such measures: the overlapping coefficient, the Kolmogorov-Smirnov distance, and the Lévy distance., Methods: We performed simulation studies to estimate the association between these three and several mean based measures for balance and bias (i.e., discrepancy between the true and the estimated treatment effect)., Results: For large sample sizes (n = 2000) the average Pearson's correlation coefficients between bias and Kolmogorov-Smirnov distance (r = 0.89), the Lévy distance (r = 0.89) and the absolute standardized mean difference (r = 0.90) were similar, whereas this was lower for the overlapping coefficient (r = -0.42). When sample size decreased to 400, mean based measures of balance had stronger correlations with bias. Models including all confounding variables, their squares and interaction terms resulted in smaller bias than models that included only main terms for confounding variables., Conclusions: We conclude that measures for balance are useful for reporting the amount of balance reached in propensity score analysis and can be helpful in selecting the final PS model., (Copyright © 2011 John Wiley & Sons, Ltd.)

Introduction: The aim of this study was to compare the outcomes of women who were initially managed by intrauterine balloon tamponade or uterine artery embolization because of persistent postpartum hemorrhage demanding an immediate intervention to control bleeding.Material and Methods: Propensity score-matched cohort study including women who had intrauterine balloon tamponade or uterine artery embolization as initial management strategy to control persistent postpartum hemorrhage, that is, refractory to first-line therapy combined with at least one uterotonic agent. The primary outcome measure was a composite of peripartum hysterectomy and/or maternal mortality. Secondary outcomes measures were total volume of blood loss and total number of packed red blood cells transfused.Results: Our 1:1 propensity score-matched cohort comprised of 50 women who had intrauterine balloon tamponade and 50 women who underwent uterine artery embolization at a blood loss between 1000 and 7000 mL. There was no statistically significant difference in the hysterectomy risk between the two groups (n = 6 in each group, odds ratio [OR] 1.00, 95% confidence interval [CI] .30-3.34), in total volume of blood loss (median 4500 mL, interquartile range [IQR] 3600-5400) for balloon vs 4000 mL (IQR 3250-5000) for embolization, P = 0.382) or in total units of packed red blood cells transfused (median 7 (IQR 5-10) for balloon vs 6 [IQR 4-9] for embolization, P = 0.319). Fifteen women (30%) who were initially managed by an intrauterine balloon still underwent uterine artery embolization, of whom one had an embolization-related thrombo-embolic event. Maternal mortality occurred in neither of the intervention groups.Conclusions: No difference in the risk of peripartum hysterectomy and/or maternal death was observed between women who had intrauterine balloon tamponade and women who underwent uterine artery embolization as an initial management for persistent postpartum hemorrhage. Although this study was underpowered to demonstrate equivalence, our study design provides a framework for future research in which intrauterine balloon tamponade may prove to be a suitable intervention of first choice in the management of persistent postpartum hemorrhage. [ABSTRACT FROM AUTHOR]

In observational studies, treatment assignment is a nonrandom process and treatment groups may not be comparable in their baseline characteristics, a phenomenon known as confounding. Propensity score (PS) methods can be used to achieve comparability of treated and nontreated groups in terms of their observed covariates and, as such, control for confounding in estimating treatment effects. In this article, we provide a step-by-step guidance on how to use PS methods. For illustrative purposes, we used simulated data based on an observational study of the relation between oral nutritional supplementation and hospital length of stay. We focused on the key aspects of PS analysis, including covariate selection, PS estimation, covariate balance assessment, treatment effect estimation, and reporting. PS matching, stratification, covariate adjustment, and weighting are discussed. R codes and example data are provided to show the different steps in a PS analysis. [ABSTRACT FROM AUTHOR]

Objectives: To assess the current practice of propensity score (PS) analysis in the medical literature, particularly the assessment and reporting of balance on confounders. Study Design and Setting: A PubMed search identified studies using PS methods from December 2011 through May 2012. For each article included in the review, information was extracted on important aspects of the PS such as the type of PS method used, variable selection for PS model, and assessment of balance. Results: Among 296 articles that were included in the review, variable selection for PS model was explicitly reported in 102 studies (34.4%). Covariate balance was checked and reported in 177 studies (59.8%). P-values were the most commonly used statistical tools to report balance (125 of 177, 70.6%). The standardized difference and graphical displays were reported in 45 (25.4%) and 11 (6.2%) articles, respectively. Matching on the PS was the most commonly used approach to control for confounding (68.9%), followed by PS adjustment (20.9%), PS stratification (13.9%), and inverse probability of treatment weighting (IPTW, 7.1%). Balance was more often checked in articles using PS matching and IPTW, 70.6% and 71.4%, respectively. Conclusion: The execution and reporting of covariate selection and assessment of balance is far from optimal. Recommendations on reporting of PS analysis are provided to allow better appraisal of the validity of PS-based studies. [ABSTRACT FROM AUTHOR]

Background: When profiling health care providers, adjustment for case-mix is essential. However, conventional risk adjustment methods may perform poorly, especially when provider volumes are small or events rare. Propensity score (PS) methods, commonly used in observational studies of binary treatments, have been shown to perform well when the amount of observations and/or events are low and can be extended to a multiple provider setting. The objective of this study was to evaluate the performance of different risk adjustment methods when profiling multiple health care providers that perform highly protocolized procedures, such as coronary artery bypass grafting. Methods: In a simulation study, provider effects estimated using PS adjustment, PS weighting, PS matching, and multivariable logistic regression were compared in terms of bias, coverage and mean squared error (MSE) when varying the event rate, sample size, provider volumes, and number of providers. An empirical example from the field of cardiac surgery was used to demonstrate the different methods. Results: Overall, PS adjustment, PS weighting, and logistic regression resulted in provider effects with low amounts of bias and good coverage. The PS matching and PS weighting with trimming led to biased effects and high MSE across several scenarios. Moreover, PS matching is not practical to implement when the number of providers surpasses three. Conclusions: None of the PS methods clearly outperformed logistic regression, except when sample sizes were relatively small. Propensity score matching performed worse than the other PS methods considered. [ABSTRACT FROM AUTHOR]